{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Create epochs"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "This example can be referenced by [citing the package](https://neuropsychology.github.io/NeuroKit/cite_us.html).\n",
                "\n",
                "In your experiment, participants undergo a number of trials (events) and these events are possibly of different conditions. You are wondering how can you locate these events on your signals and perhaps make them into epochs for future analysis?\n",
                "\n",
                "This example shows how to use NeuroKit to extract epochs from data based on events localisation. In case you have multiple data files for each subject, this example also shows you how to create a loop through the subject folders and put the files together in an epoch format for further analysis."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Load NeuroKit package\n",
                "import neurokit2 as nk"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {
                "tags": [
                    "remove-input"
                ]
            },
            "outputs": [],
            "source": [
                "# Note: this cell is hidden using the \"remove-input\" tag\n",
                "# Make bigger images\n",
                "import matplotlib.pyplot as plt\n",
                "plt.rcParams['figure.figsize'] = [15, 5]  \n",
                "plt.rcParams['font.size']= 14"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "In this example, we will use a short segment of data which has ECG, EDA and respiration (RSP) signals."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## One signal with multiple event markings"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Retrieve ECG data from data folder (sampling rate= 1000 Hz)\n",
                "data = nk.data(\"bio_eventrelated_100hz\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Besides the signal channels, this data also has a fourth channel which consists of a string of 0 and 5. This is a binary marking of the Digital Input channel in BIOPAC. \n",
                "\n",
                "Let's visualize the event-marking channel below."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "[<matplotlib.lines.Line2D at 0x25be3ddd070>]"
                        ]
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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",
                        "text/plain": [
                            "<Figure size 1080x360 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Visualize the event-marking channel\n",
                "nk.signal_plot(data['Photosensor'])"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Depending on how you set up your experiment, the onset of the event can either be marked by signal going from 0 to 5 or vice versa. Specific to this data, the onsets of the events are marked where the signal in the event-marking channel goes from 5 to 0 and the offsets of the events are marked where the signal goes from 0 to 5.\n",
                "\n",
                "As shown in the above figure, there are four times the signal going from 5 to 0, corresponding to the 4 events (4 trials) in this data.  \n",
                "\n",
                "There were 2 types (the condition) of images that were shown to the participant: “Negative” vs. “Neutral” in terms of emotion. Each condition had 2 trials. The following list is the condition order."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [],
            "source": [
                "condition_list = [\"Negative\", \"Neutral\", \"Neutral\", \"Negative\"]"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Before we can epoch the data, we have to locate the events and extract their related information. This can be done using NeuroKit function [events_find()](https://neuropsychology.github.io/NeuroKit/functions/events.html#events-find)."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "{'onset': array([ 1024,  4957,  9224, 12984]),\n",
                            " 'duration': array([300, 300, 300, 300]),\n",
                            " 'label': array(['1', '2', '3', '4'], dtype='<U11'),\n",
                            " 'condition': ['Negative', 'Neutral', 'Neutral', 'Negative']}"
                        ]
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# Find events\n",
                "events = nk.events_find(event_channel=data[\"Photosensor\"], \n",
                "                        threshold_keep='below', \n",
                "                        event_conditions=condition_list)\n",
                "\n",
                "events"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "The output of [events_find()](https://neuropsychology.github.io/NeuroKit/functions/events.html#events-find) gives you a `dictionary` that contains the information of event onsets, event duration, event label and event condition. \n",
                "\n",
                "As stated, as the event onsets of this data are marked by event channel going from 5 to **0**, the `threshold_keep` is set to `below`. Depends on your data, you can customize the `arguments` in [events_find()](https://neuropsychology.github.io/NeuroKit/functions/events.html#events-find) to correctly locate the events. \n",
                "\n",
                "You can use the [events_plot()](https://neuropsychology.github.io/NeuroKit/functions/events.html#events-plot) function to plot the events that have been found together with your event channel to confirm that it is correct."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1080x360 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plot = nk.events_plot(events, data['Photosensor'])"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Or you can visualize the events together with the all other signals."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1080x360 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plot = nk.events_plot(events, data)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "After you have located the events, you can now create epochs using the NeuroKit [epochs_create()](https://neuropsychology.github.io/NeuroKit/functions/epochs.html#epochs-create) function. However, we recommend to process your signal first before cutting them to smaller epochs. You can read more about processing of physiological signals using NeuroKit in [Custom your Processing Pipeline](https://neuropsychology.github.io/NeuroKit/examples/bio_custom/bio_custom.html) Example."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Process the signal\n",
                "df, info = nk.bio_process(ecg=data[\"ECG\"], rsp=data[\"RSP\"], eda=data[\"EDA\"], sampling_rate=100)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Now, let's think about how we want our epochs to be like. For this example, we want:\n",
                "  \n",
                "    1. Epochs to start *1 second before the event onset*\n",
                "\n",
                "    2. Epochs to end *6 seconds* afterwards\n",
                "\n",
                "These are passed into the `epochs_start` and `epochs_end` arguments, respectively. \n",
                "\n",
                "Our epochs will then cover the region from **-1 s** to **+6 s** relative to the onsets of events (i.e., 700 data points since the signal is sampled at 100Hz)."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Build and plot epochs\n",
                "epochs = nk.epochs_create(df, events, sampling_rate=100, epochs_start=-1, epochs_end=6)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "And as easy as that, you have created a dictionary of four dataframes, each correspond to an epoch of the event. \n",
                "\n",
                "Here, in the above example, all your epochs have the same starting time and ending time, specified by `epochs_start` and `epochs_end`. Nevertheless, you can also pass a list of different timings to these two arguments to customize the duration of the epochs for each of your events. "
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Each subject with multiple files"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "In some experimental designs, instead of having one signal file with multiple events, each subject can have multiples files where each file is the record of one event. \n",
                "\n",
                "In the following example, we will show you how to create a loop through the subject folders and put the files together in an epoch format for further analysis. "
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Firstly, let's say your data is arranged as the following where each subject has a folder and in each folder there are multiple data files corresponding to different events:"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "```\n",
                "[Experiment folder]\n",
                "|\n",
                "└── Data\n",
                "|   |\n",
                "|   └── Subject_001/\n",
                "|   |   │   event_1.[csv]\n",
                "|   |   │   event_2.[csv]\n",
                "|   |   |__ ......\n",
                "|   └── Subject_002/\n",
                "|       │   event_1.[csv]\n",
                "|       │   event_2.[csv]\n",
                "|       |__ ......\n",
                "└── analysis_script.py\n",
                "```"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "The following will illustrate how your analysis script might look like. Try to re-create such data structure and the analysis script in your computer! "
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Assuming that your working directory is now at your analysis script, and you want to read all the data files of `Subject_001`. \n",
                "\n",
                "Your analysis script should look something like below (you need to uncomment the code):"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# # Load packages\n",
                "# import pandas as pd\n",
                "# import os\n",
                "\n",
                "# # Your working directory should be at Experiment folder\n",
                "# participant = \"Subject_001\"\n",
                "\n",
                "# sampling_rate = 100\n",
                "\n",
                "# # List all data files in Subject_001 folder\n",
                "# all_files = os.listdir(\"Data/\" + participant)\n",
                "\n",
                "# # Create an empty directory to store your files (events)\n",
                "# epochs = {}\n",
                "\n",
                "# # Loop through each file in the subject folder\n",
                "# for i, file in enumerate(all_files):\n",
                "#     # Read the file\n",
                "#     data = pd.read_csv(\"Data/\" + participant + \"/\" + file)\n",
                "#     # Add a Label column (e.g Label 1 for epoch 1)\n",
                "#     data[\"Label\"] = np.full(len(data), str(i + 1))\n",
                "#     # Set index of data to time in seconds\n",
                "#     index = data.index / sampling_rate\n",
                "#     data = data.set_index(pd.Series(index))\n",
                "#     # Append the file into the dictionary\n",
                "#     epochs[str(i + 1)] = data\n",
                "\n",
                "# epochs"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "And tah dah! You now should have a `dictionary` called epochs that resembles the output of NeuroKit [epochs_create()](https://neuropsychology.github.io/NeuroKit/examples/misc_epochs_create/misc_epochs_create.html). Each `DataFrame` in the epochs corresponds to an event (a trial) that `Subject_001` underwent. \n",
                "\n",
                "The epochs is now ready for further analysis!"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "language": "python",
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        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
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            "file_extension": ".py",
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